a. The Field of the Invention
This invention relates to the field of processing materials to determine their constituent components. In particular, the invention relates to automatically identifying lanes of separated sample materials.
b. Background Information
Gel electrophoresis, and other molecular separation techniques, allow scientists to determine the molecular sequence of biological materials. For example, gel electrophoreses is used to determine the DNA sequence of a given sample of biological matter. DNA sequencing involves determining the sequence of nucleotides that form the DNA.
Generally, gel electrophoresis involves placing a gel material on a glass plate. The gel has a number of wells at one end of the plate. These wells are filled with the sample of the biological material and a number of dyes. When excited with light, the dyes fluoresce. The samples react with the dyes to make partial copies of samples. Each partial copy contains from 1-N nucleotides of the DNA sequence of the sample (where N is the number of nucleotides in the sample). Thus, each partial copy has a weight proportional to the number of nucleotides in that copy. A goal of gel electrophoresis is to separate the partial copies in order of their weight. The type of dye at the end of each partial copy indicates the type of the last nucleotide in that partial copy. The type of dye can be determined from the dye's fluorescent color. The glass plate is placed between two poles of an electric field. Under the effects of the magnetic field, the partial copies move through the gel. The partial copies move at rates proportional to their weights. Thus, longer partial copies move more slowly than shorter partial copies. The partial copies separate according to their weight, and therefore, length. The separated partial copies separate into lanes of bands, each band corresponding to a different length of partial copy. By reading the color of the bands in order, the DNA sequence can be determined.
Automated computer systems have been developed that automatically locate the lanes, and ultimately the bands in images of the gels. The band information is what is important to the scientist. However, to identify the bands, the lanes must be accurately identified. An example of a system that used alpha-beta tracking is the Sequence Analysis.TM. system available from PE Applied Biosystems, Inc. However, many of these systems do not work well for some gel images. The problem arises with the inconsistent shape of the lanes produced in the gels. For example, the lanes of bands are not necessarily straight; they may veer off to one side of the gel or the other.
One example of a previous system used a technique called alpha-beta tracking. In alpha-beta tracking, a program would start with what it thought were two bands in a lane. The program then finds the direction of flow between those two bands. The program then projects out a probability field of where a subsequent band in the lane should be. The corresponding area in the gel image is then searched for a band. The program then selects a band in that area as being the most likely next band in the lane. The process repeats until the end of the lane is reached. The problem with alpha-beta tracking is that two bands can fall within the probability area. In certain circumstances, alpha-beta tracking will select the wrong band. Once the wrong band is selected, the correct lane will not be identified.
Alpha-beta tracking performs particularly poorly in some applications of gel electrophoresis, such as gene scanning (e.g., STR analysis). In gene scanning, the lanes may have large gaps between bands. (For comparison, in DNA sequencing, long lines of bands appear in the lanes. Generally, there is a band in a lane for each possible length of a partial copy of the DNA matter.) In gene scanning applications, there may be many large gaps between bands in a lane. The relatively few number of bands makes the automatic tracking of the lanes difficult and often causes previous systems to require human intervention to identify lanes.
The problem of requiring human intervention for lane identification is that it increases the cost of processing gels and also decreases the through put of the systems. The need for human intervention can become the bottleneck in some situations. Also, as the number of lanes per gel increases and the number of runs per day increases, the problem compounds.
Therefore it is desirable to have an improved automated process for determining where the lanes are in a gel of samples. The process should recognize a higher percentage of lanes than presently available systems. Additionally, where a human is still required to identify lanes, the system should provide an improved interface so that less intervention is required.